The last post in this series, Making Apps that Learn and Adapt, described the shift from building AI/ML solutions to building apps that learn and adapt. But, how does the app learn? And as a developer, how do I teach it what it should learn?

With Spice.ai, we teach the app how to learn using a Spicepod.

Imagine you own a restaurant. You created a menu, hired staff, constructed the kitchen and dining room, and got off to a great start when it first opened. However, over the years, your customers' tastes changed, you've had to make compromises on ingredients, and there's a hot new place down the street... business is stagnating, and you know that you need to make some changes to stay competitive.

You have a few options. First, you could gather all the data, such as customer surveyss, seasonal produce metrics, and staff performance profiles. You may even hire outside consultants. You then take this data to your office, and after spending some time organizing, filtering, and collating it, you've discovered an insight! Your seafood dishes sell poorly and cost the most... you are losing money! You spend several weeks or months perfecting a new menu, which you roll out with much fanfare! And then… business is still poor. What!? How could this be? It was a data-driven approach! You start the process again. While this approach is a worthy option, it has long latency from data to learning to implementation.

Another option is to build real-time learning and adaption directly into the restaurant. Imagine a staff member whose sole job was learning and adapting how the restaurant should operate; lets name them Blue. You write a guide for Blue that defines certain goal metrics, like customer food ratings, staff happiness, and of course, profit. Blue tracks each dish served, from start to finish, from who prepared it to its temperature, its costs, and its final customer taste rating. Blue not only learns from each customer review as each dish is consumed but also how dish preparation affects other goal metrics, like profitability. The restaurant staff consults Blue to determine any adjustments to improve goal metrics as they work. The latency from data to learning, to adaption, has been reduced, from weeks or months to minutes. This option, of course, is not feasible for most restaurants, but software applications can use this approach. Blue and his instructions are analogous to the Spice.ai runtime and manifest.

In the Spice.ai model, developers teach the app how to learn by describing goals and rewarding its actions, much like how a parent might teach a child. As these rewards are applied in training, the app learns what actions maximize its rewards towards the defined goals.

Returning to the restaurant example, you can think of the Spice.ai runtime as Blue, and Spicepod manifests as the guide on how Blue should learn. Individual staff members would consult with Blue for ongoing recommendations on decisions to make and how to act. These goals and rewards are defined in Spicepods or "pods" for short. Spicepods are packages of configuration that describe the application's goals and how it should learn from data. Although it's not a direct analogy, Spicepods and their manifests can be conceptualized similar to Docker containers and Dockerfiles. In contrast, Dockerfiles define the packaging of your app, Spicepods specify the packaging of your app's learning and data.

Anatomy of a Spicepod

A Spicepod consists of:

• A required YAML manifest that describes how the pod should learn from data
• Optional seed data
• Learned model/state
• Performance telemetry and metrics

Developers author Spicepods using the spice CLI command such as with spice pod init <name> or simply by creating a manifest file such as mypod.yaml in the spicepods directory of their application.

Here's an example of the Tweet Recommendation Quickstart Spicepod manifest.

A screenshot of the Spicepod manifest for the Tweet Recommendation Quickstart

You can see the data definitions under dataspaces, the actions the application may take under actions, and their rewards when training.

In the next post, I'll walk through in detail each section of the pod manifest. In the meantime, you can review the documentation for a complete reference of the Spicepod manifest syntax.

Spicepods as packages

On disk, Spicepods are generally layouts of a manifest file, seed data, and trained models, but they can also be exported as zipped packages.

A screenshot of the Spicepod layout for the trader quickstart application

When the runtime exports a Spicepod using the spice export command, it is saved with a .spicepod extension. It can then be shared, archived, or imported into another instance of the Spice.ai runtime.

Soon, we also expect to enable publishing of .spicepods to spicerack.org, from where community-created Spicepods can easily be added to your application using spice add <pod name> (currently, only Spice AI published pods are available on spicerack.org).

Treating Spicepods as packages and enabling their sharing and distribution through spicerack.org will help developers share their "restaurant guides" and build upon each other's work, much like they do with npmjs.org or pypi.org. In this way, developers can together build better and more intelligent applications.

In the next post, we'll dive deeper into authoring a Spicepod manifest to create an intelligent application. Follow @spice_ai on Twitter to get an update when we post.

If you haven't already, read the next the first post in the series, Making Apps that Learn and Adapt.

Learn more and contribute​

Building intelligent apps that leverage AI is still way too hard, even for advanced developers. Our mission is to make this as easy as creating a modern web page. If the vision resonates with you, join us!

Our Spice.ai Roadmap is public, and now that we have launched, the project and work are open for collaboration.

If you are interested in partnering, we'd love to talk. Try out Spice.ai, email us "hey," get in touch on Discord, or reach out on Twitter.

We are just getting started! 🚀

Luke

We are excited to announce the release of Spice.ai v0.4-alpha! 🏄‍♂️

Highlights include support for authoring reward functions in a code file, the ability to specify the time of recommendation, and ingestion support for transaction/correlation ids. Authoring reward functions in a code file is a significant improvement to the developer experience than specifying functions inline in the YAML manifest, and we are looking forward to your feedback on it!

If you are new to Spice.ai, check out the getting started guide and star spiceai/spiceai on GitHub.

Highlights in v0.4-alpha​

Upgrade using spice upgrade​

The spice upgrade command was added in the v0.3.1-alpha release, so you can now upgrade from v0.3.1 to v0.4 by simply running spice upgrade in your terminal. Special thanks to community member @Adm28 for contributing this feature!

Reward Function Files​

In addition to defining reward code inline, it is now possible to author reward code in functions in a separate Python file.

The reward function file path is defined by the reward_funcs property.

A function defined in the code file is mapped to an action by authoring its name in the with property of the relevant reward.

Example:

training:
  reward_funcs: my_reward.py
  rewards:
    - reward: buy
      with: buy_reward
    - reward: sell
      with: sell_reward
    - reward: hold
      with: hold_reward

Learn more in the documentation: docs.spiceai.org/concepts/rewards/external

Time Categories​

Spice.ai can now learn from cyclical patterns, such as daily, weekly, or monthly cycles.

To enable automatic cyclical field generation from the observation time, specify one or more time categories in the pod manifest, such as a month or weekday in the time section.

For example, by specifying month the Spice.ai engine automatically creates a field in the AI engine data stream called time_month_{month} with the value calculated from the month of which that timestamp relates.

Example:

time:
  categories:
    - month
    - dayofweek

Supported category values are: month dayofmonth dayofweek hour

Learn more in the documentation: docs.spiceai.org/reference/pod/#time

Get recommendation for a specific time​

It is now possible to specify the time of recommendations fetched from the /recommendation API.

Valid times are from pod epoch_time to epoch_time + period.

Previously the API only supported recommendations based on the time of the last ingested observation.

Requests are made in the following format: GET http://localhost:8000/api/v0.1/pods/{pod}/recommendation?time={unix_timestamp}

An example for quickstarts/trader

GET http://localhost:8000/api/v0.1/pods/trader/recommendation?time=1605729600

Specifying {unix_timestamp} as 0 will return a recommendation based on the latest data. An invalid {unix_timestamp} will return a result that has the valid time range in the error message:

{
  "response": {
    "result": "invalid_recommendation_time",
    "message": "The time specified (1610060201) is outside of the allowed range: (1610057600, 1610060200)",
    "error": true
  }
}

New in this release​

• Adds time categories configuration to the pod manifest to enable learning from cyclical patterns in data - e.g. hour, day of week, day of month, and month
• Adds support for defining reward functions in a rewards functions code file.
• Adds the ability to specify recommendation time making it possible to now see which action Spice.ai recommends at any time during the pod period.
• Adds support for ingestion of transaction/correlation identifiers (e.g. order_id, trace_id) in the pod manifest.
• Adds validation for invalid dataspace names in the pod manifest.
• Adds the ability to resize columns to the dashboard observation data grid.
• Updates to TensorFlow 2.7 and Keras 2.7
• Fixes a bug where data processors were using data connector params
• Fixes a dashboard issue in the pod observations data grid where a column might not be shown.
• Fixes a crash on pod load if the training section is not included in the manifest.
• Fixes an issue where data manager stats errors were incorrectly being printed to console.
• Fixes an issue where selectors may not match due to surrounding whitespace.

Resources​

• Getting started with Spice.ai
• Documentation
• Quickstarts and Samples

Community​

Spice.ai started with the vision to make AI easy for developers. We are building Spice.ai in the open and with the community. Reach out on Discord or by email to get involved. We will also be starting a community call series soon!

• Discord: https://discord.gg/kZnTfneP5u
• Reddit: https://www.reddit.com/r/spiceai
• Twitter: @spice_ai
• Email: [email protected]

In the Spice.ai announcement blog post, we shared some of the inspiration for the project stemming from challenges in applying and integrating AI/ML into a neurofeedback application. Building upon those ideas, in this post, we explore the shift in approach from a focus of data science and machine learning (ML) to apps that learn and adapt.

As a developer, I've followed the AI/ML space with keen interest and been impressed with the advances and announcements that only seem to be increasing. stateof.ai recently published its 2021 report, and once again, it's been another great year of progress. At the same time, it's still more challenging than ever for mainstream developers to integrate AI/ML into their applications. For most developers, where AI/ML is not their full-time job, and without the support of a dedicated ML team, creating and developing an intelligent application that learns and adapts is still too hard.

Most solutions on the market, even those that claim they are for developers, focus on helping make ML easier instead of making it easier to build applications. These solutions have been great for advancing ML itself but have not helped developers leverage ML in their apps to make them intelligent. Even when a developer successfully integrates ML into an application, it might make that application smart, but often does not help the app continue to learn and adapt over time.

Traditionally, the industry has viewed AI/ML as separate from the application. A pipeline, service, or team is provided with data, which trains on that data, and can then provide answers or insights. These solutions are often created with a waterfall-like approach, gathering and defining requirements, designing, implementing, testing, and deploying. Sometimes this process can take months or even years.

With Spice.ai, we propose a new approach to building applications. By bringing AI/ML alongside your compute and data and incorporating it as part of your application, the app can incrementally adopt recommendations from the AI engine and in addition the AI engine can learn from the application's data and actions. This approach shifts from waterfall-like to agile-like, where the AI engine ingests streams of application and external data, along with the results of the application's actions, to continuously learn. This virtuous feedback cycle from the app to the AI engine and back again enables the app to get smarter and adapt over time. In this approach, building your application is developing the ML.

Being part of the application is not just conceptual. Development teams deploy the Spice.ai runtime and AI engine with the application as a sidecar or microservice, enabling the app services and runtime to work together and for data to be kept application local. A developer teaches the AI engine how to learn by defining application goals and rewards for actions the application takes. The AI Engine observes the application and the consequences of its actions, which feeds into its experience. As the AI engine learns, the application can adapt.

As developers shift from thinking about disparate applications and ML to building applications where AI that learns and adapts is integrated as a core part of the application logic, a new class of intelligent applications will emerge. And as technical talent becomes even more scarce, applications built this way will be necessary, not just to be competitive but to be even built at all.

In the next post, I'll discuss the concept of Spicepods, bundles of configuration that describes how the application should learn, and how the Spice.ai runtime hosts and uses them to help developers make applications that learn.

Learn more and contribute​

Building intelligent apps that leverage AI is still way too hard, even for advanced developers. Our mission is to make this as easy as creating a modern web page. If the vision resonates with you, join us!

Our Spice.ai Roadmap is public, and now that we have launched, the project and work are open for collaboration.

If you are interested in partnering, we'd love to talk. Try out Spice.ai, email us "hey," get in touch on Discord, or reach out on Twitter.

We are just getting started! 🚀

Luke

We are excited to announce the release of Spice.ai v0.3.1-alpha! 🎃

This point release focuses on fixes and improvements to v0.3-alpha. Highlights include the ability to specify both seed and runtime data, to select custom named fields for time and tags, a new spice upgrade command and several bug fixes.

A special acknowledgment to @Adm28, who added the new spice upgrade command, which enables the CLI to self-update, which in turn will auto-update the runtime.

Highlights in v0.3.1-alpha​

Upgrade command​

The CLI can now be updated using the new spice upgrade command. This command will check for, download, and install the latest Spice.ai CLI release, which will become active on it's next run.

When run, the CLI will check for the matching version of the Spice.ai runtime, and will automatically download and install it as necessary.

The version of both the Spice.ai CLI and runtime can be checked with the spice version CLI command.

Seed data​

When working with streaming data sources, like market prices, it's often also useful to seed the dataspace with historical data. Spice.ai enables this with the new seed_data node in the dataspace configuration. The syntax is exactly the same as the data syntax. For example:

dataspaces:
  - from: coinbase
    name: btcusd
    seed_data:
      connector: file
        params:
          path: path/to/seed/data.csv
      processor:
        name: csv
    data:
      connector: coinbase
        params:
          product_ids: BTC-USD
      processor:
        name: json

The seed data will be fetched first, before the runtime data is initialized. Both sets of connectors and processors use the dataspace scoped measurements, categories and tags for processing, and both data sources are merged in pod-scoped observation timeline.

Time field selectors​

Before v0.3.1-alpha, data was required to include a specific time field. In v0.3.1-alpha, the JSON and CSV data processors now support the ability to select a specific field to populate the time field. An example selector to use the created_at column for time is:

data:
  processor:
    name: csv
    params:
      time_selector: created_at

Tag field selectors​

Before v0.3.1-alpha, tags were required to be placed in a _tags field. In v0.3.1-alpha, any field can now be selected to populate tags. Tags are pod-unique string values, and the union of all selected fields will make up the resulting tag list. For example:

dataspace:
  from: twitter
  name: tweets
  tags:
    selectors:
      - tags
      - author_id
    values:
      - spice_ai
      - spicy

New in this release​

• Adds a new spice upgrade command for self-upgrade of the Spice.ai CLI.
• Adds a new seed_data node to the dataspace configuration, enabling the dataspace to be seeded with an alternative source of data.
• Adds the ability to select a custom time field in JSON and CSV data processors with the time_selector parameter.
• Adds the ability to select custom tag fields in the dataspace configuration with selectors list.
• Adds error reporting for AI engine crashes, where previously it would fail silently.
• Fixes the dashboard pods list from "jumping" around due to being unsorted.
• Fixes rare cases where categorical data might be sent to the AI engine in the wrong format.

Resources​

• Getting started with Spice.ai
• Documentation
• Quickstarts and Samples

Community​

Spice.ai started with the vision to make AI easy for developers. We are building Spice.ai in the open and with the community. Reach out on Discord or by email to get involved. We will also be starting a community call series soon!

• Discord: https://discord.gg/kZnTfneP5u
• Reddit: https://www.reddit.com/r/spiceai
• Twitter: @spice_ai
• Email: [email protected]

We are excited to announce the release of Spice.ai v0.3-alpha! 🎉

This release adds support for ingestion, automatic encoding, and training of categorical data, enabling more use-cases and datasets beyond just numerical measurements. For example, perhaps you want to learn from data that includes a category of t-shirt sizes, with discrete values, such as small, medium, and large. The v0.3 engine now supports this and automatically encodes the categorical string values into numerical values that the AI engine can use. Also included is a preview of data visualizations in the dashboard, which is helpful for developers as they author Spicepods and dataspaces.

A screenshot of the data visualization preview

A special acknowledgment to @sboorlagadda, who submitted the first Spice.ai feature contribution from the community ever! He added the ability to list pods from the CLI with the new spice pods list command. Thank you, @sboorlagadda!!!

A screenshot of the new spice pods list command and output.

If you are new to Spice.ai, check out the getting started guide and star spiceai/spiceai on GitHub.

Highlights in v0.3-alpha​

Categorical data​

In v0.1, the runtime and AI engine only supported ingesting numerical data. In v0.2, tagged data was accepted and automatically encoded into fields available for learning. In this release, v0.3, categorical data can now also be ingested and automatically encoded into fields available for learning. This is a breaking change with the format of the manifest changing separating numerical measurements and categorical data.

Pre-v0.3, the manifest author specified numerical data using the fields node.

In v0.3, numerical data is now specified under measurements and categorical data under categories. E.g.

dataspaces:
  - from: event
    name: stream
    measurements:
      - name: duration
        selector: length_of_time
        fill: none
      - name: guest_count
        selector: num_guests
        fill: none
    categories:
      - name: event_type
        values:
          - dinner
          - party
      - name: target_audience
        values:
          - employees
          - investors
    tags:
      - tagA
      - tagB

Data visualizations preview​

A top piece of community feedback was the ability to visualize data. After first running Spice.ai, we'd often hear from developers, "how do I see the data?". A preview of data visualizations is now included in the dashboard on the pod page.

Listing pods​

Once the Spice.ai runtime has started, you can view the loaded pods on the dashboard and fetch them via API call localhost:8000/api/v0.1/pods. To make it even easier, we've added the ability to list them via the CLI with the new spice pods list command, which shows the list of pods and their manifest paths.

Coinbase data connector​

A new Coinbase data connector is included in v0.3, enabling the streaming of live market ticker prices from Coinbase Pro. Enable it by specifying the coinbase data connector and providing a list of Coinbase Pro product ids. E.g. "BTC-USD". A new sample which demonstrates is also available with its associated Spicepod available from the spicerack.org registry. Get it with spice add samples/trader

Tweet Recommendation Quickstart​

A new Tweet Recommendation Quickstart has been added. Given past tweet activity and metrics of a given account, this app can recommend when to tweet, comment, or retweet to maximize for like count, interaction rates, and outreach of said given Twitter account.

Trader Sample​

A new Trader Sample has been added in addition to the Trader Quickstart. The sample uses the new Coinbase data connector to stream live Coinbase Pro ticker data for learning.

New in this release​

• Adds support for ingesting, encoding, and training on categorical data. v0.3 uses one-hot-encoding.
• Changes Spicepod manifest fields node to measurements and add the categories node.
• Adds the ability to select a field from the source data and map it to a different field name in the dataspace. See an example for measurements in docs.
• Adds support for JSON content type when fetching from the /observations API. Previously, only CSV was supported.
• Adds a preview version of data visualizations to the dashboard. The grid has several limitations, one of which is it currently cannot be resized.
• Adds the ability to select which learning algorithm to use via the CLI, the API, and specified in the Spicepod manifest. Possible choices are currently "vpg", Vanilla Policy Gradient and "dql", Deep Q-Learning. Shout out to @corentin-pro, who added this feature on his second day on the team!
• Adds the ability to list loaded pods with the CLI command spice pods list.
• Adds a new coinbase data connector for Coinbase Pro market prices.
• Adds a new Tweet Recommendation Quickstart.
• Adds a new Trader Sample.
• Fixes bug where the /observations endpoint was not providing fully qualified field names.
• Fixes issue where debugging messages were printed when using spice add.

Resources​

• Getting started with Spice.ai
• Documentation
• Quickstarts and Samples

Community​

Spice.ai started with the vision to make AI easy for developers. We are building Spice.ai in the open and with the community. Reach out on Discord or by email to get involved. We will also be starting a community call series soon!

• Discord: https://discord.gg/kZnTfneP5u
• Reddit: https://www.reddit.com/r/spiceai
• Twitter: @spice_ai
• Email: [email protected]